| collisions_overall | speed_overall | distance_overall | |
|---|---|---|---|
| speed_overall | -0.04 | ||
| distance_overall | 0.24 | 0.04 | |
| distance_overall_deviation | 0.24 | 0.04 | 1.00*** |
## Some items ( 2 4 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' optionSome items ( 2 4 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
## [[1]]
## [1] "collisions = 0.83"
##
## [[2]]
## [1] "speed = 0.88"
##
## [[3]]
## [1] "distance = 0.17"
##
## [[4]]
## [1] "distance_deviation = 0.17"
| collisions_no_fog_overall | speed_no_fog_overall | |
|---|---|---|
| speed_no_fog_overall | 0.34* | |
| distance_no_fog_overall | 0.24 | -0.03 |
## Some items ( 1 5 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
## [[1]]
## [1] "collisions_no_fog = 0.81"
##
## [[2]]
## [1] "speed_no_fog = 0.75"
##
## [[3]]
## [1] "distance_no_fog = 0.04"
| collisions_fog_overall | speed_fog_overall | |
|---|---|---|
| speed_fog_overall | -0.05 | |
| distance_fog_overall | 0.27* | 0.08 |
## Some items ( 2 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
## [[1]]
## [1] "collisions_fog = 0.66"
##
## [[2]]
## [1] "speed_fog = 0.77"
##
## [[3]]
## [1] "distance_fog = -0.05"
| var | mean_overall | sd_overall | mean_driver | sd_driver | mean_co | sd_co |
|---|---|---|---|---|---|---|
| agreeableness | 3.9189815 | 0.6319277 | 3.98 | 0.63 | 3.86 | 0.63 |
| bias | 3.0441277 | 20.5145140 | 3.12 | 19.66 | 2.96 | 21.52 |
| confidence | 60.1323343 | 19.0227797 | 58.37 | 19.54 | 61.89 | 18.51 |
| congruent_errors | 0.9345794 | 1.3823075 | 0.93 | 1.55 | 0.94 | 1.20 |
| congruent_time | 461.5544460 | 51.0173810 | 448.12 | 50.44 | 475.24 | 48.31 |
| conscientiousness | 3.2384259 | 0.7499099 | 3.10 | 0.76 | 3.38 | 0.72 |
| discrimination | 27.2903277 | 19.4473443 | 28.85 | 19.66 | 25.74 | 19.29 |
| driving_years | 2.7171296 | 4.9146936 | 3.28 | 4.75 | 2.16 | 5.06 |
| extraversion | 3.1759259 | 0.7138968 | 3.18 | 0.73 | 3.17 | 0.70 |
| gaming_time | 1.1231481 | 1.0136320 | 1.26 | 1.05 | 0.99 | 0.96 |
| gf_accuracy | 57.0882066 | 21.9362721 | 55.25 | 21.92 | 58.93 | 22.00 |
| incongruent_errors | 1.5420561 | 1.8185645 | 1.63 | 1.78 | 1.45 | 1.87 |
| incongruent_time | 532.1606055 | 59.6359822 | 514.92 | 62.99 | 549.72 | 50.82 |
| inhibitory_cost | 70.6061595 | 30.0274495 | 66.80 | 31.09 | 74.49 | 28.68 |
| intellect | 3.6018519 | 0.6654324 | 3.56 | 0.65 | 3.64 | 0.69 |
| neuroticism | 2.7962963 | 0.7323852 | 2.82 | 0.75 | 2.77 | 0.72 |
| repeat_errors | 1.6574074 | 1.7356193 | 1.78 | 1.66 | 1.54 | 1.82 |
| repeat_time | 920.7157111 | 244.1991035 | 930.65 | 241.58 | 910.78 | 248.65 |
| resilience | 3.6137500 | 0.3568200 | 3.64 | 0.33 | 3.59 | 0.38 |
| switch_cost | 125.2960244 | 144.8566483 | 103.81 | 116.58 | 146.79 | 166.82 |
| switch_errors | 2.0370370 | 2.1352665 | 2.11 | 2.00 | 1.96 | 2.28 |
| switch_time | 1046.0117355 | 304.7873181 | 1034.45 | 289.04 | 1057.57 | 322.07 |
| wm_accuracy | 43.2716049 | 18.5563131 | 40.86 | 19.43 | 45.68 | 17.49 |
| agreeableness | bias | confidence | congruent_errors | congruent_time | conscientiousness | discrimination | driving_years | extraversion | gaming_time | gf_accuracy | incongruent_errors | incongruent_time | inhibitory_cost | intellect | neuroticism | repeat_errors | repeat_time | resilience | switch_cost | switch_errors | switch_time | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| bias | -0.07 | |||||||||||||||||||||
| confidence | 0.01 | 0.39*** | ||||||||||||||||||||
| congruent_errors | 0.11 | 0.18 | 0.03 | |||||||||||||||||||
| congruent_time | 0.03 | -0.18 | -0.48*** | -0.11 | ||||||||||||||||||
| conscientiousness | 0.06 | 0.09 | -0.05 | -0.06 | 0.17 | |||||||||||||||||
| discrimination | -0.07 | -0.35*** | 0.03 | -0.09 | -0.17 | -0.01 | ||||||||||||||||
| driving_years | 0.05 | -0.29** | -0.25* | -0.09 | 0.28** | 0.13 | -0.04 | |||||||||||||||
| extraversion | -0.03 | 0.09 | -0.03 | -0.10 | 0.10 | -0.04 | -0.06 | 0.05 | ||||||||||||||
| gaming_time | 0.16 | -0.07 | 0.22* | -0.16 | -0.17 | -0.22* | 0.24* | -0.17 | -0.18 | |||||||||||||
| gf_accuracy | 0.07 | -0.60*** | 0.51*** | -0.14 | -0.24* | -0.12 | 0.36*** | 0.06 | -0.11 | 0.26** | ||||||||||||
| incongruent_errors | 0.04 | 0.14 | 0.14 | 0.26** | -0.52*** | -0.19 | 0.08 | -0.19 | -0.04 | 0.07 | -0.01 | |||||||||||
| incongruent_time | -0.03 | -0.19 | -0.47*** | -0.21* | 0.86*** | 0.14 | -0.13 | 0.26** | 0.03 | -0.17 | -0.23* | -0.41*** | ||||||||||
| inhibitory_cost | -0.11 | -0.07 | -0.11 | -0.23* | 0.02 | -0.02 | 0.01 | 0.04 | -0.11 | -0.04 | -0.03 | 0.07 | 0.52*** | |||||||||
| intellect | -0.06 | -0.05 | 0.20* | 0.07 | -0.10 | -0.17 | 0.14 | -0.04 | -0.06 | 0.29** | 0.21* | 0.07 | -0.09 | 0.00 | ||||||||
| neuroticism | 0.04 | -0.12 | 0.02 | 0.08 | 0.03 | 0.02 | 0.09 | -0.04 | 0.00 | -0.10 | 0.13 | 0.12 | 0.05 | 0.04 | 0.07 | |||||||
| repeat_errors | -0.11 | 0.16 | -0.13 | 0.22* | -0.15 | -0.24* | -0.16 | -0.13 | -0.09 | -0.15 | -0.27** | 0.43*** | -0.09 | 0.08 | -0.02 | 0.03 | ||||||
| repeat_time | -0.02 | -0.09 | -0.37*** | -0.14 | 0.51*** | 0.10 | -0.10 | 0.15 | 0.10 | -0.14 | -0.24* | -0.25* | 0.41*** | -0.05 | -0.03 | 0.17 | -0.08 | |||||
| resilience | 0.08 | -0.02 | 0.06 | -0.12 | -0.09 | 0.14 | 0.05 | 0.06 | 0.17 | -0.04 | 0.07 | 0.08 | -0.12 | -0.08 | -0.10 | -0.37*** | -0.01 | -0.15 | ||||
| switch_cost | -0.17 | -0.29** | -0.25** | -0.10 | 0.38*** | -0.02 | 0.08 | 0.20* | 0.09 | -0.13 | 0.06 | -0.25** | 0.32*** | -0.01 | -0.14 | -0.08 | -0.11 | 0.17 | 0.08 | |||
| switch_errors | -0.03 | 0.30** | -0.18 | 0.25** | -0.01 | -0.18 | -0.15 | -0.13 | -0.02 | -0.10 | -0.44*** | 0.30** | 0.02 | 0.06 | -0.01 | 0.02 | 0.58*** | -0.12 | -0.07 | -0.13 | ||
| switch_time | -0.10 | -0.21* | -0.42*** | -0.16 | 0.59*** | 0.07 | -0.04 | 0.21* | 0.13 | -0.17 | -0.16 | -0.32*** | 0.48*** | -0.04 | -0.09 | 0.10 | -0.12 | 0.88*** | -0.09 | 0.61*** | -0.16 | |
| wm_accuracy | -0.03 | 0.04 | 0.36*** | -0.08 | -0.15 | 0.02 | 0.05 | -0.13 | -0.09 | 0.14 | 0.27** | 0.07 | -0.14 | -0.03 | 0.35*** | 0.10 | -0.07 | -0.14 | 0.06 | 0.07 | -0.11 | -0.08 |
## [1] "resilience = 0.79"
## [1] "extraversion = 0.7"
## [1] "agreeableness = 0.59"
## [1] "conscientiousness = 0.65"
## [1] "neuroticism = 0.68"
## [1] "intellect = 0.61"
## [[1]]
## [1] "extraversion = 0.7"
##
## [[2]]
## [1] "agreeableness = 0.59"
##
## [[3]]
## [1] "conscientiousness = 0.65"
##
## [[4]]
## [1] "neuroticism = 0.68"
##
## [[5]]
## [1] "intellect = 0.61"
## [1] "RAPM accuracy = 0.83"
## [1] "RAPM confidence = 0.93"
## [1] "RAPM bias = 0.85"
## [1] "RAPM discrimination = 0.59"
## Some items ( 14 16 19 24 25 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "repeat errors = 0.49"
## Some items ( 1 2 6 7 10 11 12 14 16 18 20 22 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "switch errors = 0.39"
## [[1]]
## [1] "repeat errors = 0.49"
##
## [[2]]
## [1] "switch errors = 0.39"
## [1] "repeat time = 0.84"
## [1] "switch time = 0.87"
## [[1]]
## [1] "repeat time = 0.84"
##
## [[2]]
## [1] "switch time = 0.87"
## [1] "switch cost = 0.01"
## [1] "working memory accuracy = 0.7"
## Some items ( 1 4 6 7 8 9 11 12 13 14 15 16 17 19 23 25 28 30 31 32 34 40 44 45 46 49 55 56 60 63 67 71 72 79 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "congruent errors = 0.53"
## Some items ( 20 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "incongruent errors = 0.59"
## [[1]]
## [1] "congruent errors = 0.53"
##
## [[2]]
## [1] "incongruent errors = 0.59"
## [1] "congruent time = 0.98"
## [1] "incongruent time = 0.82"
## [[1]]
## [1] "congruent time = 0.98"
##
## [[2]]
## [1] "incongruent time = 0.82"
## [1] "inhibitory cost = 0.95"
## Some items ( 18 20 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "resilience = 0.77"
## [1] "extraversion = 0.76"
## [1] "agreeableness = 0.64"
## [1] "conscientiousness = 0.69"
## [1] "neuroticism = 0.68"
## [1] "intellect = 0.59"
## [[1]]
## [1] "extraversion = 0.76"
##
## [[2]]
## [1] "agreeableness = 0.64"
##
## [[3]]
## [1] "conscientiousness = 0.69"
##
## [[4]]
## [1] "neuroticism = 0.68"
##
## [[5]]
## [1] "intellect = 0.59"
## [1] "RAPM accuracy = 0.83"
## [1] "RAPM confidence = 0.94"
## [1] "RAPM bias = 0.84"
## [1] "RAPM discrimination = 0.45"
## Some items ( 2 3 4 8 9 13 14 17 19 21 23 24 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "switch errors = 0.3"
## Some items ( 2 3 4 5 7 8 9 10 11 12 13 14 19 21 22 26 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "repeat errors = 0.26"
## [[1]]
## [1] "switch errors = 0.3"
##
## [[2]]
## [1] "repeat errors = 0.26"
## [1] "switch time = 0.88"
## [1] "repeat time = 0.86"
## [[1]]
## [1] "switch time = 0.88"
##
## [[2]]
## [1] "repeat time = 0.86"
## [1] "switch cost = -0.21"
## Some items ( 15 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "working memory accuracy = 0.73"
## Some items ( 7 8 9 11 12 13 14 16 22 23 28 30 32 34 44 45 56 67 72 79 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "congruent errors = 0.64"
## Some items ( 1 6 11 17 20 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "incongruent errors = 0.56"
## [[1]]
## [1] "congruent errors = 0.64"
##
## [[2]]
## [1] "incongruent errors = 0.56"
## [1] "congruent time = 0.99"
## [1] "incongruent time = 0.86"
## [[1]]
## [1] "congruent time = 0.99"
##
## [[2]]
## [1] "incongruent time = 0.86"
## [1] "inhibitory cost = 0.97"
## [1] "resilience = 0.82"
## [1] "extraversion = 0.65"
## [1] "agreeableness = 0.54"
## [1] "conscientiousness = 0.59"
## [1] "neuroticism = 0.68"
## [1] "intellect = 0.63"
## [[1]]
## [1] "extraversion = 0.65"
##
## [[2]]
## [1] "agreeableness = 0.54"
##
## [[3]]
## [1] "conscientiousness = 0.59"
##
## [[4]]
## [1] "neuroticism = 0.68"
##
## [[5]]
## [1] "intellect = 0.63"
## [1] "RAPM accuracy = 0.84"
## [1] "RAPM confidence = 0.92"
## [1] "RAPM bias = 0.85"
## [1] "RAPM discrimination = 0.72"
## Some items ( 6 9 10 11 12 14 16 17 18 19 24 25 26 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "repeat errors = 0.62"
## Some items ( 3 4 5 8 10 12 13 15 16 17 19 20 21 22 23 24 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "switch errors = 0.48"
## [[1]]
## [1] "repeat errors = 0.62"
##
## [[2]]
## [1] "switch errors = 0.48"
## [1] "repeat time = 0.82"
## [1] "switch time = 0.87"
## [[1]]
## [1] "repeat time = 0.82"
##
## [[2]]
## [1] "switch time = 0.87"
## [1] "switch cost = 0.09"
## [1] "working memory accuracy = 0.66"
## Some items ( 1 3 4 6 9 11 12 14 15 17 19 22 23 24 26 31 38 41 42 49 55 57 59 60 71 72 75 76 77 79 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "congruent errors = 0.36"
## Some items ( 2 11 19 20 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option[1] "incongruent errors = 0.64"
## [[1]]
## [1] "congruent errors = 0.36"
##
## [[2]]
## [1] "incongruent errors = 0.64"
## [1] "congruent time = 0.95"
## [1] "incongruent time = 0.74"
## [[1]]
## [1] "congruent time = 0.95"
##
## [[2]]
## [1] "incongruent time = 0.74"
## [1] "inhibitory cost = 0.93"
| co_info_help_overall | co_info_harm_overall | co_instruct_help_overall | co_instruct_harm_overall | co_redundant_overall | co_question_overall | drive_question_overall | drive_informs_overall | |
|---|---|---|---|---|---|---|---|---|
| co_info_harm_overall | 0.31* | |||||||
| co_instruct_help_overall | 0.41** | 0.03 | ||||||
| co_instruct_harm_overall | 0.13 | 0.43** | 0.48*** | |||||
| co_redundant_overall | 0.11 | 0.36** | 0.25 | 0.30* | ||||
| co_question_overall | 0.38** | 0.03 | 0.49*** | 0.45*** | 0.16 | |||
| drive_question_overall | 0.63*** | 0.23 | 0.41** | 0.15 | 0.21 | 0.31* | ||
| drive_informs_overall | 0.52*** | 0.17 | 0.45*** | 0.35** | 0.20 | 0.72*** | 0.38** | |
| drive_frust_overall | 0.14 | 0.39** | 0.30* | 0.30* | 0.33* | 0.11 | 0.28* | 0.26 |
## [[1]]
## [1] "co_info_help = 0.84"
##
## [[2]]
## [1] "co_info_harm = 0.73"
##
## [[3]]
## [1] "co_instruct_help = 0.9"
##
## [[4]]
## [1] "co_instruct_harm = 0.52"
##
## [[5]]
## [1] "co_redundant = 0.73"
##
## [[6]]
## [1] "co_question = 0.8"
##
## [[7]]
## [1] "drive_question = 0.8"
##
## [[8]]
## [1] "drive_informs = 0.85"
##
## [[9]]
## [1] "drive_frust = 0.89"
Given the moderate to high correlations between the communication variables, it looks like there is a multicollinearity issue. Need to reduce comms variables to a smaller number using factor analysis.
Reduce variables to smaller number of factors and correlate with each other. It looks like 3 components can be extracted from the comms variables.
| co_info_help_overall | co_info_harm_overall | co_instruct_help_overall | co_instruct_harm_overall | co_redundant_overall | co_question_overall | drive_question_overall | drive_informs_overall | |
|---|---|---|---|---|---|---|---|---|
| co_info_harm_overall | 0.31* | |||||||
| co_instruct_help_overall | 0.41** | 0.03 | ||||||
| co_instruct_harm_overall | 0.13 | 0.43** | 0.48*** | |||||
| co_redundant_overall | 0.11 | 0.36** | 0.25 | 0.30* | ||||
| co_question_overall | 0.38** | 0.03 | 0.49*** | 0.45*** | 0.16 | |||
| drive_question_overall | 0.63*** | 0.23 | 0.41** | 0.15 | 0.21 | 0.31* | ||
| drive_informs_overall | 0.52*** | 0.17 | 0.45*** | 0.35** | 0.20 | 0.72*** | 0.38** | |
| drive_frust_overall | 0.14 | 0.39** | 0.30* | 0.30* | 0.33* | 0.11 | 0.28* | 0.26 |
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = cor(pca, use = "complete.obs"))
## Overall MSA = 0.65
## MSA for each item =
## co_info_help_overall co_info_harm_overall co_instruct_help_overall
## 0.61 0.44 0.67
## co_instruct_harm_overall co_redundant_overall co_question_overall
## 0.58 0.78 0.69
## drive_question_overall drive_informs_overall drive_frust_overall
## 0.77 0.74 0.67
## [1] "Bartletts test of spherecity"
## chisq p.value df
## 1 176.0047 2.388931e-20 36
| communalities | |
|---|---|
| co_info_help_overall | 0.82 |
| co_info_harm_overall | 0.70 |
| co_instruct_help_overall | 0.59 |
| co_instruct_harm_overall | 0.74 |
| co_redundant_overall | 0.49 |
| co_question_overall | 0.81 |
| drive_question_overall | 0.76 |
| drive_informs_overall | 0.70 |
| drive_frust_overall | 0.52 |
| component | eigen | prop_var | cum_var | rotation_SS_load |
|---|---|---|---|---|
| 1 | 3.54 | 0.27 | 0.27 | 2.42 |
| 2 | 1.47 | 0.22 | 0.49 | 1.98 |
| 3 | 1.12 | 0.19 | 0.68 | 1.74 |
| 4 | 0.78 | |||
| 5 | 0.68 | |||
| 6 | 0.62 | |||
| 7 | 0.37 | |||
| 8 | 0.22 | |||
| 9 | 0.19 |
| var | PC1 | PC2 | PC3 |
|---|---|---|---|
| co_question_overall | 0.95 | ||
| drive_informs_overall | 0.73 | ||
| co_instruct_help_overall | 0.71 | ||
| co_instruct_harm_overall | 0.62 | 0.46 | -0.33 |
| co_info_harm_overall | 0.87 | ||
| drive_frust_overall | 0.71 | ||
| co_redundant_overall | 0.68 | ||
| co_info_help_overall | 0.85 | ||
| drive_question_overall | 0.81 |
## inconsistent terrible helpful
## inconsistent 1.00 0.38 0.33
## terrible 0.38 1.00 0.17
## helpful 0.33 0.17 1.00
## component r
## 1 inconsistent_codriver 0.9998
## 2 terrible_codriver 0.9996
## 3 helpful_exchange 0.9997
| collisions_overall | speed_overall | distance_overall | inconsistent_codriver | terrible_codriver | helpful_exchange | age_co_driver | age_driver | aus_born_co_driver | aus_born_driver | aus_years_co_driver | aus_years_driver | dic_use_co_driver | dic_use_driver | eng_fl_co_driver | eng_fl_driver | sex_co_driver | sex_driver | prop_female | driving_years | gaming_time | congruent_errors | congruent_time | incongruent_errors | incongruent_time | inhibitory_cost | repeat_errors | repeat_time | switch_errors | switch_time | switch_cost | wm_accuracy | resilience | gf_accuracy | confidence | bias | discrimination | agreeableness | conscientiousness | extraversion | intellect | neuroticism | driving_years_drone | gaming_time_drone | congruent_errors_drone | congruent_time_drone | incongruent_errors_drone | incongruent_time_drone | inhibitory_cost_drone | repeat_errors_drone | repeat_time_drone | switch_errors_drone | switch_time_drone | switch_cost_drone | wm_accuracy_drone | resilience_drone | gf_accuracy_drone | confidence_drone | bias_drone | discrimination_drone | agreeableness_drone | conscientiousness_drone | extraversion_drone | intellect_drone | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| speed_overall | -0.04 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| distance_overall | 0.24 | 0.04 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| inconsistent_codriver | 0.28* | -0.27* | 0.27* | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| terrible_codriver | 0.32* | -0.09 | 0.38** | 0.34* | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| helpful_exchange | 0.07 | -0.40** | 0.20 | 0.32* | 0.17 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| age_co_driver | 0.03 | 0.10 | 0.19 | -0.02 | -0.05 | 0.12 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| age_driver | -0.01 | -0.04 | 0.00 | -0.11 | 0.07 | 0.20 | -0.08 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| aus_born_co_driver | -0.05 | -0.09 | 0.07 | 0.20 | -0.17 | 0.30* | -0.12 | 0.02 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| aus_born_driver | -0.03 | 0.01 | -0.09 | 0.04 | 0.02 | 0.00 | 0.14 | -0.27* | -0.03 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| aus_years_co_driver | 0.19 | -0.20 | 0.35 | 0.31 | -0.18 | 0.25 | 0.60** | -0.06 | NANA | 0.07 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
| aus_years_driver | -0.01 | -0.29 | 0.11 | 0.11 | 0.04 | 0.65* | -0.22 | 0.47 | 0.04 | NANA | -0.02 | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| dic_use_co_driver | -0.40 | -0.29 | 0.02 | -0.23 | -0.07 | 0.17 | 0.15 | 0.18 | 0.02 | -0.22 | -0.30 | 0.83 | ||||||||||||||||||||||||||||||||||||||||||||||||||||
| dic_use_driver | -0.16 | 0.36 | -0.04 | -0.25 | -0.09 | -0.36 | 0.55 | -0.21 | -0.56 | -0.72* | -0.10 | -0.89* | 0.16 | |||||||||||||||||||||||||||||||||||||||||||||||||||
| eng_fl_co_driver | 0.01 | -0.22 | 0.11 | 0.18 | -0.31* | 0.30* | 0.06 | 0.10 | 0.53*** | 0.01 | 0.57** | 0.23 | NANA | -0.04 | ||||||||||||||||||||||||||||||||||||||||||||||||||
| eng_fl_driver | -0.27 | 0.15 | -0.10 | -0.17 | -0.23 | -0.03 | 0.10 | -0.19 | 0.09 | 0.40** | -0.09 | -0.12 | -0.36 | NANA | 0.11 | |||||||||||||||||||||||||||||||||||||||||||||||||
| sex_co_driver | -0.22 | 0.08 | -0.08 | 0.06 | -0.04 | 0.05 | -0.11 | -0.07 | 0.07 | 0.14 | -0.12 | 0.14 | 0.18 | -0.37 | -0.04 | 0.09 | ||||||||||||||||||||||||||||||||||||||||||||||||
| sex_driver | -0.28* | 0.58*** | 0.07 | -0.21 | -0.13 | -0.24 | 0.11 | -0.19 | 0.06 | 0.00 | -0.01 | -0.41 | -0.12 | 0.19 | -0.20 | 0.04 | 0.22 | |||||||||||||||||||||||||||||||||||||||||||||||
| prop_female | 0.32* | -0.47*** | 0.00 | 0.13 | 0.12 | 0.15 | -0.02 | 0.18 | -0.08 | -0.07 | 0.08 | 0.34 | -0.03 | 0.03 | 0.16 | -0.07 | -0.70*** | -0.85*** | ||||||||||||||||||||||||||||||||||||||||||||||
| driving_years | -0.08 | -0.06 | -0.03 | -0.10 | 0.03 | 0.19 | -0.07 | 0.95*** | -0.03 | -0.25 | -0.12 | 0.51 | 0.53* | -0.16 | 0.16 | -0.07 | -0.04 | -0.26 | 0.22 | |||||||||||||||||||||||||||||||||||||||||||||
| gaming_time | 0.12 | -0.01 | 0.24 | 0.05 | 0.09 | 0.19 | -0.07 | -0.05 | 0.08 | -0.08 | 0.02 | 0.04 | 0.24 | 0.06 | -0.13 | -0.39** | 0.15 | 0.29* | -0.29* | -0.16 | ||||||||||||||||||||||||||||||||||||||||||||
| congruent_errors | 0.07 | -0.13 | -0.22 | -0.17 | -0.22 | -0.18 | 0.00 | -0.19 | 0.03 | 0.06 | 0.05 | 0.22 | -0.29 | 0.25 | 0.15 | 0.19 | -0.08 | -0.04 | 0.07 | -0.17 | -0.20 | |||||||||||||||||||||||||||||||||||||||||||
| congruent_time | 0.04 | -0.11 | -0.18 | 0.01 | 0.00 | 0.08 | -0.24 | 0.28* | 0.10 | -0.04 | -0.17 | 0.37 | -0.14 | -0.20 | 0.09 | -0.09 | -0.09 | -0.36** | 0.31* | 0.32* | -0.29* | -0.11 | ||||||||||||||||||||||||||||||||||||||||||
| incongruent_errors | 0.37** | -0.01 | 0.34* | 0.10 | -0.01 | 0.03 | 0.42** | -0.20 | 0.00 | 0.00 | 0.43* | -0.05 | -0.01 | -0.01 | 0.12 | 0.01 | 0.01 | 0.08 | -0.06 | -0.23 | 0.12 | 0.26 | -0.47*** | |||||||||||||||||||||||||||||||||||||||||
| incongruent_time | 0.15 | -0.03 | -0.15 | 0.02 | 0.03 | 0.05 | -0.17 | 0.25 | 0.09 | -0.01 | -0.22 | 0.21 | -0.22 | 0.02 | 0.02 | -0.07 | -0.07 | -0.36** | 0.30* | 0.28* | -0.25 | -0.22 | 0.87*** | -0.38** | ||||||||||||||||||||||||||||||||||||||||
| inhibitory_cost | 0.24 | 0.11 | 0.00 | 0.01 | 0.06 | -0.02 | 0.04 | 0.05 | 0.02 | 0.05 | -0.21 | -0.35 | -0.26 | 0.71* | -0.11 | 0.00 | 0.01 | -0.14 | 0.09 | 0.06 | -0.03 | -0.27 | 0.15 | 0.00 | 0.61*** | |||||||||||||||||||||||||||||||||||||||
| repeat_errors | 0.21 | -0.04 | 0.11 | 0.10 | 0.08 | 0.06 | 0.33* | -0.21 | -0.20 | 0.00 | 0.54** | -0.18 | -0.11 | 0.15 | 0.04 | 0.05 | 0.06 | 0.01 | -0.04 | -0.19 | -0.19 | 0.21 | -0.07 | 0.47*** | -0.03 | 0.04 | ||||||||||||||||||||||||||||||||||||||
| repeat_time | 0.09 | -0.05 | -0.04 | 0.15 | 0.14 | 0.06 | -0.18 | 0.09 | 0.10 | 0.08 | -0.30 | 0.32 | -0.38 | -0.34 | 0.02 | -0.05 | 0.04 | -0.21 | 0.13 | 0.14 | -0.20 | -0.13 | 0.62*** | -0.28* | 0.48*** | -0.03 | -0.10 | |||||||||||||||||||||||||||||||||||||
| switch_errors | 0.24 | -0.05 | -0.04 | -0.12 | 0.00 | -0.23 | 0.09 | -0.25 | -0.26 | 0.23 | 0.29 | -0.14 | -0.19 | -0.01 | 0.04 | 0.24 | -0.08 | -0.19 | 0.18 | -0.22 | -0.31* | 0.36** | 0.06 | 0.27 | 0.02 | -0.07 | 0.43** | -0.06 | ||||||||||||||||||||||||||||||||||||
| switch_time | 0.02 | 0.00 | -0.17 | 0.10 | 0.13 | 0.01 | -0.17 | 0.16 | 0.02 | 0.02 | -0.36 | 0.33 | -0.42 | -0.41 | -0.11 | -0.05 | -0.02 | -0.13 | 0.11 | 0.20 | -0.24 | -0.21 | 0.70*** | -0.39** | 0.57*** | 0.01 | -0.14 | 0.92*** | -0.08 | |||||||||||||||||||||||||||||||||||
| switch_cost | -0.14 | 0.11 | -0.34* | -0.07 | 0.04 | -0.09 | -0.04 | 0.20 | -0.14 | -0.11 | -0.34 | 0.14 | -0.29 | -0.24 | -0.30* | -0.03 | -0.12 | 0.10 | -0.01 | 0.21 | -0.18 | -0.25 | 0.45*** | -0.38** | 0.41** | 0.10 | -0.13 | 0.21 | -0.06 | 0.58*** | ||||||||||||||||||||||||||||||||||
| wm_accuracy | -0.12 | -0.06 | 0.11 | 0.17 | 0.10 | 0.03 | -0.13 | 0.02 | 0.06 | -0.24 | -0.14 | -0.21 | 0.08 | 0.52 | 0.09 | -0.04 | 0.15 | 0.13 | -0.18 | 0.02 | 0.18 | -0.11 | -0.15 | 0.03 | -0.08 | 0.08 | -0.11 | -0.24 | -0.05 | -0.19 | 0.03 | |||||||||||||||||||||||||||||||||
| resilience | 0.02 | 0.20 | -0.03 | 0.16 | 0.24 | 0.11 | -0.03 | 0.19 | -0.20 | -0.21 | 0.35 | 0.17 | 0.32 | 0.32 | -0.09 | -0.39** | -0.03 | 0.22 | -0.15 | 0.13 | 0.00 | -0.14 | -0.05 | 0.09 | -0.08 | -0.08 | 0.25 | -0.21 | -0.09 | -0.09 | 0.21 | 0.26 | ||||||||||||||||||||||||||||||||
| gf_accuracy | -0.14 | 0.15 | 0.16 | 0.12 | 0.11 | 0.10 | 0.09 | 0.20 | 0.03 | -0.19 | -0.13 | -0.26 | 0.24 | 0.26 | -0.07 | -0.20 | 0.09 | 0.34* | -0.29* | 0.20 | 0.40** | -0.25 | -0.32* | -0.13 | -0.21 | 0.10 | -0.26 | -0.18 | -0.45*** | -0.15 | 0.02 | 0.30* | 0.12 | |||||||||||||||||||||||||||||||
| confidence | -0.10 | 0.06 | 0.20 | 0.14 | 0.10 | -0.05 | 0.14 | -0.19 | -0.21 | -0.03 | 0.14 | -0.43 | 0.17 | 0.45 | -0.10 | -0.14 | -0.06 | 0.20 | -0.12 | -0.22 | 0.38** | -0.08 | -0.57*** | 0.00 | -0.49*** | -0.07 | -0.21 | -0.32* | -0.18 | -0.39** | -0.29* | 0.35* | 0.02 | 0.56*** | ||||||||||||||||||||||||||||||
| bias | 0.06 | -0.11 | 0.03 | 0.00 | -0.02 | -0.16 | 0.05 | -0.42** | -0.24 | 0.18 | 0.38 | -0.24 | -0.15 | 0.29 | -0.03 | 0.09 | -0.15 | -0.17 | 0.21 | -0.44*** | -0.07 | 0.20 | -0.21 | 0.14 | -0.26 | -0.18 | 0.08 | -0.12 | 0.32* | -0.22 | -0.31* | 0.01 | -0.10 | -0.56*** | 0.37** | |||||||||||||||||||||||||||||
| discrimination | -0.06 | 0.21 | 0.08 | -0.14 | -0.13 | -0.06 | 0.07 | -0.10 | 0.01 | 0.10 | -0.12 | -0.23 | 0.04 | -0.53 | -0.17 | -0.06 | 0.34* | 0.38** | -0.46*** | -0.15 | 0.32* | -0.14 | -0.22 | 0.13 | -0.20 | -0.04 | -0.13 | -0.02 | -0.06 | -0.01 | 0.01 | 0.10 | -0.05 | 0.32* | 0.11 | -0.25 | ||||||||||||||||||||||||||||
| agreeableness | 0.12 | -0.33* | 0.05 | 0.33* | 0.15 | 0.45*** | -0.15 | 0.13 | 0.32* | 0.10 | 0.13 | 0.64* | 0.12 | -0.51 | 0.37** | -0.05 | -0.01 | -0.29* | 0.22 | 0.14 | 0.13 | 0.15 | 0.06 | 0.06 | 0.01 | -0.07 | -0.07 | 0.07 | 0.03 | -0.04 | -0.24 | 0.06 | 0.08 | 0.17 | 0.04 | -0.15 | -0.04 | |||||||||||||||||||||||||||
| conscientiousness | -0.15 | -0.07 | 0.05 | 0.02 | 0.05 | 0.02 | -0.21 | 0.15 | 0.05 | 0.05 | -0.10 | 0.56* | 0.34 | -0.60 | 0.09 | -0.09 | -0.08 | -0.03 | 0.07 | 0.15 | -0.20 | 0.09 | 0.19 | -0.11 | 0.13 | -0.05 | -0.15 | 0.06 | 0.00 | 0.04 | -0.03 | 0.03 | 0.24 | -0.13 | -0.15 | 0.00 | 0.00 | 0.28* | ||||||||||||||||||||||||||
| extraversion | -0.16 | 0.00 | -0.08 | 0.12 | -0.11 | 0.17 | 0.11 | -0.01 | -0.03 | 0.23 | 0.06 | 0.12 | -0.29 | -0.41 | -0.10 | 0.23 | -0.03 | -0.19 | 0.15 | 0.07 | -0.33* | -0.17 | 0.12 | -0.03 | 0.06 | -0.07 | -0.05 | 0.26 | 0.03 | 0.26 | 0.11 | -0.15 | -0.04 | -0.23 | -0.19 | 0.07 | 0.09 | -0.11 | -0.08 | |||||||||||||||||||||||||
| intellect | 0.04 | 0.04 | -0.08 | -0.22 | 0.03 | -0.11 | -0.06 | 0.01 | 0.11 | -0.01 | -0.21 | -0.20 | -0.09 | 0.67* | 0.01 | 0.08 | 0.05 | 0.06 | -0.07 | 0.01 | 0.23 | 0.28* | -0.17 | 0.05 | -0.10 | 0.07 | -0.08 | -0.12 | 0.05 | -0.15 | -0.13 | 0.40** | -0.02 | 0.19 | 0.19 | -0.02 | 0.11 | 0.07 | -0.28* | -0.24 | ||||||||||||||||||||||||
| neuroticism | 0.12 | 0.04 | 0.26 | -0.05 | -0.12 | -0.18 | 0.09 | -0.12 | -0.02 | -0.08 | 0.03 | -0.23 | 0.00 | 0.49 | 0.17 | 0.08 | -0.18 | -0.05 | 0.13 | -0.04 | -0.24 | 0.16 | 0.00 | 0.18 | 0.05 | 0.11 | 0.07 | 0.14 | 0.22 | 0.00 | -0.28* | -0.03 | -0.34* | 0.02 | -0.03 | -0.06 | 0.09 | -0.04 | 0.06 | 0.07 | 0.00 | |||||||||||||||||||||||
| driving_years_drone | 0.05 | 0.11 | 0.21 | 0.03 | -0.12 | 0.11 | 0.95*** | -0.07 | -0.07 | 0.09 | 0.66*** | -0.23 | -0.47 | 0.41 | 0.18 | 0.09 | -0.06 | 0.15 | -0.07 | -0.09 | -0.07 | 0.03 | -0.25 | 0.43** | -0.19 | 0.02 | 0.32* | -0.20 | 0.07 | -0.21 | -0.11 | -0.12 | 0.08 | 0.07 | 0.14 | 0.06 | 0.12 | -0.07 | -0.15 | 0.06 | -0.09 | 0.10 | ||||||||||||||||||||||
| gaming_time_drone | -0.05 | 0.00 | -0.05 | -0.10 | -0.03 | -0.05 | -0.19 | 0.21 | 0.07 | 0.05 | -0.28 | 0.19 | 0.24 | 0.14 | -0.08 | -0.03 | 0.32* | 0.04 | -0.20 | 0.20 | 0.21 | -0.02 | -0.21 | -0.01 | -0.11 | 0.12 | 0.04 | -0.14 | 0.02 | -0.22 | -0.26 | 0.12 | 0.04 | 0.13 | -0.04 | -0.19 | 0.04 | 0.13 | -0.12 | 0.01 | 0.24 | 0.03 | -0.22 | |||||||||||||||||||||
| congruent_errors_drone | -0.06 | 0.07 | 0.02 | -0.09 | -0.18 | 0.07 | -0.02 | 0.13 | 0.12 | -0.18 | -0.37 | 0.35 | -0.09 | 0.07 | 0.08 | 0.23 | 0.02 | 0.08 | -0.07 | 0.21 | -0.07 | 0.07 | 0.07 | 0.01 | 0.06 | 0.01 | -0.22 | 0.03 | -0.31* | 0.03 | 0.02 | 0.13 | -0.16 | -0.01 | -0.07 | -0.06 | 0.14 | -0.18 | 0.05 | 0.14 | 0.05 | 0.08 | 0.00 | -0.12 | ||||||||||||||||||||
| congruent_time_drone | -0.06 | -0.03 | -0.06 | -0.12 | -0.26 | -0.14 | 0.32* | -0.06 | -0.08 | 0.14 | 0.34 | -0.39 | -0.45 | 0.23 | 0.22 | 0.00 | -0.19 | 0.00 | 0.10 | -0.06 | -0.03 | 0.20 | -0.24 | 0.23 | -0.18 | 0.02 | 0.11 | -0.20 | 0.21 | -0.25 | -0.19 | 0.04 | -0.09 | -0.01 | 0.17 | 0.17 | -0.19 | -0.02 | -0.02 | -0.03 | 0.03 | -0.02 | 0.34* | 0.02 | -0.12 | |||||||||||||||||||
| incongruent_errors_drone | -0.07 | 0.04 | 0.07 | 0.06 | 0.11 | 0.01 | -0.14 | -0.06 | -0.11 | 0.06 | -0.33 | 0.41 | 0.27 | 0.14 | -0.21 | 0.14 | 0.30* | 0.11 | -0.24 | 0.01 | 0.09 | 0.02 | 0.16 | -0.08 | 0.05 | -0.15 | -0.03 | 0.14 | -0.02 | 0.11 | -0.02 | -0.09 | -0.11 | 0.14 | -0.02 | -0.17 | 0.26 | 0.04 | 0.01 | -0.10 | 0.06 | 0.16 | -0.16 | 0.00 | 0.27 | -0.59*** | ||||||||||||||||||
| incongruent_time_drone | 0.01 | -0.07 | -0.02 | -0.12 | -0.28* | -0.14 | 0.32* | -0.10 | 0.00 | 0.07 | 0.43 | -0.26 | -0.35 | 0.42 | 0.21 | -0.03 | -0.23 | 0.05 | 0.09 | -0.13 | 0.04 | 0.40** | -0.31* | 0.38** | -0.30* | -0.10 | 0.06 | -0.25 | 0.23 | -0.32* | -0.27* | 0.09 | 0.06 | 0.04 | 0.17 | 0.12 | -0.11 | 0.17 | 0.10 | -0.13 | 0.14 | 0.03 | 0.35* | 0.01 | -0.22 | 0.83*** | -0.47*** | |||||||||||||||||
| inhibitory_cost_drone | 0.11 | -0.07 | 0.06 | -0.01 | -0.05 | -0.02 | 0.03 | -0.08 | 0.13 | -0.12 | 0.11 | 0.07 | 0.24 | 0.44 | 0.00 | -0.05 | -0.09 | 0.08 | -0.01 | -0.14 | 0.11 | 0.37** | -0.15 | 0.29* | -0.23 | -0.22 | -0.08 | -0.10 | 0.06 | -0.15 | -0.17 | 0.10 | 0.24 | 0.09 | 0.03 | -0.07 | 0.12 | 0.34* | 0.22 | -0.18 | 0.21 | 0.09 | 0.05 | -0.02 | -0.19 | -0.21 | 0.16 | 0.37** | ||||||||||||||||
| repeat_errors_drone | 0.00 | 0.01 | 0.06 | 0.17 | 0.25 | 0.04 | -0.12 | -0.03 | 0.04 | 0.07 | -0.36 | 0.57* | 0.25 | -0.19 | -0.05 | 0.14 | 0.06 | 0.11 | -0.11 | -0.01 | -0.05 | 0.21 | -0.02 | 0.13 | -0.06 | -0.08 | 0.09 | 0.04 | -0.04 | -0.01 | -0.10 | 0.14 | 0.17 | 0.13 | -0.08 | -0.23 | 0.19 | 0.20 | 0.27* | -0.05 | 0.10 | 0.27 | -0.09 | -0.13 | 0.23 | -0.20 | 0.39** | -0.11 | 0.13 | |||||||||||||||
| repeat_time_drone | 0.01 | 0.26 | 0.13 | -0.07 | -0.02 | 0.03 | 0.23 | -0.08 | -0.15 | -0.07 | 0.15 | -0.33 | 0.02 | 0.71* | 0.01 | 0.01 | -0.13 | 0.28* | -0.13 | -0.07 | 0.04 | -0.01 | -0.21 | 0.23 | -0.19 | -0.04 | 0.25 | -0.08 | 0.16 | -0.11 | -0.10 | 0.05 | 0.19 | 0.14 | 0.19 | 0.03 | -0.26 | -0.12 | -0.05 | -0.08 | -0.04 | 0.05 | 0.15 | -0.09 | -0.16 | 0.46*** | -0.22 | 0.41** | -0.05 | -0.07 | ||||||||||||||
| switch_errors_drone | -0.21 | -0.15 | 0.17 | 0.19 | 0.07 | 0.03 | -0.14 | 0.03 | 0.20 | 0.12 | -0.29 | 0.48 | 0.28 | -0.29 | 0.22 | 0.22 | 0.21 | 0.03 | -0.14 | 0.08 | -0.09 | 0.35* | -0.09 | 0.15 | -0.24 | -0.35** | 0.06 | 0.01 | 0.02 | -0.09 | -0.25 | 0.15 | 0.04 | 0.10 | -0.08 | -0.19 | 0.17 | 0.31* | 0.13 | -0.05 | 0.16 | 0.29* | -0.06 | 0.08 | 0.15 | -0.07 | 0.32* | 0.05 | 0.21 | 0.70*** | -0.17 | |||||||||||||
| switch_time_drone | -0.01 | 0.31* | 0.09 | 0.01 | -0.12 | 0.06 | 0.28* | -0.06 | -0.01 | 0.05 | 0.32 | -0.40 | -0.17 | 0.42 | 0.22 | 0.06 | -0.12 | 0.26 | -0.13 | -0.04 | 0.06 | -0.05 | -0.20 | 0.27* | -0.15 | 0.03 | 0.21 | -0.03 | 0.09 | -0.08 | -0.13 | 0.09 | 0.10 | 0.19 | 0.24 | 0.03 | -0.15 | 0.02 | -0.02 | -0.02 | -0.02 | 0.04 | 0.24 | -0.10 | -0.11 | 0.51*** | -0.25 | 0.43** | -0.11 | -0.10 | 0.86*** | -0.22 | ||||||||||||
| switch_cost_drone | -0.04 | 0.20 | -0.01 | 0.11 | -0.21 | 0.08 | 0.20 | 0.00 | 0.20 | 0.21 | 0.46* | -0.37 | -0.60* | -0.16 | 0.42** | 0.09 | -0.04 | 0.09 | -0.04 | 0.04 | 0.07 | -0.08 | -0.07 | 0.19 | 0.00 | 0.12 | 0.04 | 0.07 | -0.06 | 0.01 | -0.11 | 0.10 | -0.10 | 0.16 | 0.18 | 0.01 | 0.10 | 0.22 | 0.04 | 0.08 | 0.03 | 0.00 | 0.23 | -0.06 | 0.03 | 0.30* | -0.16 | 0.21 | -0.13 | -0.09 | 0.17 | -0.17 | 0.65*** | |||||||||||
| wm_accuracy_drone | -0.05 | 0.09 | -0.12 | -0.16 | 0.12 | -0.12 | -0.24 | -0.02 | -0.21 | -0.10 | -0.54** | -0.35 | 0.33 | 0.01 | -0.19 | -0.10 | 0.18 | 0.02 | -0.11 | 0.02 | 0.03 | -0.23 | 0.05 | -0.11 | 0.02 | -0.03 | -0.05 | 0.01 | -0.01 | 0.04 | 0.07 | 0.15 | 0.01 | 0.07 | 0.08 | 0.00 | -0.03 | -0.07 | -0.03 | -0.06 | 0.03 | -0.02 | -0.26 | 0.13 | -0.03 | -0.24 | 0.13 | -0.33* | -0.19 | -0.01 | -0.03 | -0.16 | 0.02 | 0.07 | ||||||||||
| resilience_drone | -0.04 | -0.22 | 0.18 | 0.14 | 0.19 | 0.28 | -0.10 | 0.15 | 0.02 | 0.05 | -0.17 | 0.50 | 0.43 | -0.63 | -0.01 | -0.30* | 0.07 | -0.19 | 0.11 | 0.10 | 0.26 | -0.24 | 0.07 | -0.16 | -0.08 | -0.26 | -0.26 | 0.27 | -0.28 | 0.26 | 0.09 | -0.05 | -0.11 | 0.04 | 0.12 | 0.07 | 0.17 | 0.12 | 0.06 | 0.12 | -0.08 | -0.22 | -0.11 | -0.11 | -0.10 | -0.10 | 0.07 | -0.14 | -0.07 | -0.19 | -0.12 | -0.07 | -0.09 | 0.01 | -0.11 | |||||||||
| gf_accuracy_drone | 0.16 | -0.03 | -0.01 | -0.17 | -0.07 | -0.07 | 0.01 | -0.19 | -0.08 | 0.14 | -0.14 | 0.37 | 0.25 | -0.53 | -0.12 | 0.09 | 0.08 | -0.15 | 0.06 | -0.13 | 0.07 | -0.18 | 0.11 | -0.03 | 0.22 | 0.26 | 0.03 | 0.09 | 0.14 | 0.09 | 0.04 | -0.19 | -0.36* | -0.12 | -0.08 | 0.06 | 0.09 | -0.04 | -0.20 | 0.11 | -0.10 | -0.07 | -0.05 | 0.14 | 0.00 | -0.23 | 0.13 | -0.33* | -0.20 | -0.27* | -0.29* | -0.43** | -0.19 | 0.06 | 0.23 | 0.04 | ||||||||
| confidence_drone | 0.19 | -0.05 | -0.11 | 0.12 | 0.23 | -0.09 | -0.24 | -0.13 | -0.16 | -0.02 | -0.57** | 0.42 | 0.27 | -0.18 | -0.30* | -0.21 | 0.19 | -0.18 | 0.02 | -0.13 | -0.04 | -0.06 | 0.24 | -0.20 | 0.24 | 0.10 | -0.12 | 0.21 | -0.03 | 0.25 | 0.18 | -0.08 | -0.07 | -0.26 | -0.15 | 0.14 | -0.03 | -0.12 | -0.03 | -0.03 | -0.06 | -0.15 | -0.26 | 0.08 | 0.19 | -0.47*** | 0.31* | -0.55*** | -0.18 | -0.05 | -0.41** | -0.18 | -0.46*** | -0.27 | 0.36** | 0.12 | 0.45*** | |||||||
| bias_drone | 0.01 | -0.02 | -0.08 | 0.28* | 0.27* | -0.01 | -0.22 | 0.09 | -0.06 | -0.16 | -0.37 | -0.17 | -0.07 | 0.57 | -0.13 | -0.27* | 0.08 | 0.00 | -0.04 | 0.03 | -0.10 | 0.13 | 0.10 | -0.15 | -0.01 | -0.19 | -0.13 | 0.09 | -0.17 | 0.12 | 0.11 | 0.13 | 0.34* | -0.10 | -0.05 | 0.06 | -0.12 | -0.06 | 0.18 | -0.14 | 0.05 | -0.05 | -0.17 | -0.08 | 0.16 | -0.17 | 0.13 | -0.13 | 0.05 | 0.23 | -0.06 | 0.29* | -0.20 | -0.29* | 0.08 | 0.06 | -0.64*** | 0.40** | ||||||
| discrimination_drone | 0.04 | -0.07 | 0.05 | -0.19 | -0.21 | 0.01 | 0.05 | 0.13 | 0.21 | -0.02 | 0.15 | 0.26 | 0.37 | -0.50 | -0.05 | 0.01 | 0.09 | -0.09 | 0.01 | 0.12 | 0.17 | -0.15 | 0.07 | 0.15 | 0.10 | 0.08 | -0.14 | 0.09 | 0.00 | 0.05 | -0.07 | -0.11 | -0.27 | -0.08 | -0.15 | -0.06 | 0.28* | 0.19 | 0.13 | 0.12 | -0.07 | -0.11 | 0.04 | 0.14 | -0.03 | -0.08 | 0.03 | -0.02 | 0.10 | -0.20 | -0.20 | -0.24 | -0.07 | 0.16 | 0.01 | 0.13 | 0.42** | -0.03 | -0.45*** | |||||
| agreeableness_drone | -0.01 | 0.01 | 0.06 | 0.14 | 0.08 | 0.03 | -0.10 | 0.11 | 0.10 | -0.25 | 0.05 | 0.05 | -0.09 | 0.52 | 0.08 | -0.07 | 0.06 | 0.04 | -0.06 | 0.05 | 0.21 | -0.11 | -0.03 | -0.10 | 0.06 | 0.17 | 0.08 | -0.12 | -0.10 | -0.11 | -0.03 | 0.12 | -0.04 | 0.10 | 0.03 | -0.08 | -0.09 | -0.14 | -0.13 | -0.26 | -0.06 | -0.16 | -0.05 | 0.17 | 0.07 | 0.06 | 0.02 | -0.01 | -0.12 | -0.16 | -0.11 | -0.09 | -0.14 | -0.11 | -0.11 | 0.05 | -0.01 | -0.01 | 0.01 | -0.12 | ||||
| conscientiousness_drone | 0.03 | 0.09 | -0.10 | 0.03 | 0.06 | 0.08 | 0.12 | 0.11 | -0.17 | -0.07 | 0.05 | -0.51 | -0.39 | 0.15 | -0.25 | -0.12 | 0.06 | 0.06 | -0.08 | 0.03 | -0.10 | -0.30* | 0.06 | -0.01 | 0.11 | 0.14 | 0.00 | -0.06 | -0.14 | 0.08 | 0.31* | -0.14 | 0.28 | -0.07 | -0.10 | -0.02 | -0.07 | -0.19 | -0.07 | 0.05 | -0.24 | -0.35* | 0.15 | -0.21 | -0.29* | 0.06 | -0.26 | 0.03 | -0.04 | -0.31* | 0.16 | -0.33* | 0.08 | -0.07 | -0.04 | 0.08 | -0.15 | 0.03 | 0.17 | 0.01 | -0.14 | |||
| extraversion_drone | -0.16 | -0.08 | 0.04 | 0.15 | 0.08 | 0.16 | 0.04 | -0.05 | 0.06 | -0.03 | 0.03 | 0.14 | 0.30 | -0.12 | 0.09 | -0.33* | -0.02 | -0.04 | 0.04 | -0.03 | 0.07 | -0.14 | 0.25 | -0.23 | 0.12 | -0.16 | 0.00 | 0.13 | -0.05 | 0.19 | 0.22 | 0.02 | 0.23 | 0.07 | -0.01 | -0.09 | 0.15 | -0.03 | 0.07 | 0.25 | -0.12 | -0.18 | 0.03 | -0.02 | -0.01 | 0.09 | -0.05 | 0.00 | -0.15 | -0.14 | -0.05 | -0.06 | 0.00 | 0.09 | -0.02 | 0.35* | 0.01 | 0.16 | 0.12 | -0.23 | 0.06 | 0.01 | ||
| intellect_drone | 0.02 | -0.08 | 0.14 | -0.11 | 0.29* | -0.13 | -0.02 | -0.01 | -0.13 | -0.01 | -0.06 | -0.24 | 0.61* | 0.30 | -0.25 | -0.14 | 0.03 | 0.00 | -0.02 | 0.00 | 0.13 | -0.18 | 0.04 | -0.05 | 0.16 | 0.26 | 0.11 | -0.18 | 0.18 | -0.16 | -0.02 | 0.16 | 0.07 | 0.18 | 0.07 | -0.13 | -0.08 | 0.07 | 0.00 | -0.17 | 0.08 | 0.01 | -0.08 | 0.38** | -0.19 | -0.07 | 0.09 | -0.12 | -0.09 | 0.05 | 0.06 | -0.06 | -0.04 | -0.17 | 0.29* | -0.15 | 0.23 | 0.20 | -0.07 | 0.18 | -0.17 | -0.10 | 0.11 | |
| neuroticism_drone | 0.05 | 0.27* | 0.13 | -0.10 | -0.06 | -0.26 | 0.02 | 0.00 | -0.08 | -0.03 | -0.28 | -0.06 | 0.09 | 0.12 | -0.11 | -0.05 | -0.15 | 0.17 | -0.04 | 0.03 | 0.06 | 0.04 | 0.03 | 0.04 | 0.11 | 0.18 | 0.00 | -0.04 | 0.09 | -0.05 | -0.03 | -0.11 | -0.09 | 0.19 | 0.09 | -0.13 | -0.12 | -0.10 | 0.12 | -0.24 | -0.06 | 0.15 | -0.04 | 0.04 | -0.04 | 0.10 | 0.06 | 0.08 | -0.03 | -0.01 | 0.20 | -0.16 | 0.19 | 0.07 | 0.26 | -0.41** | 0.25 | 0.09 | -0.18 | 0.09 | 0.11 | -0.02 | -0.08 | 0.15 |
|
|
|
## [1] "DV = inconsistent_codriver"
##
## Call:
## lm(formula = fm, data = var)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.63493 -0.58181 -0.01837 0.54393 2.70811
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.360e-16 1.243e-01 0.000 1.00000
## scale(agreeableness) 3.448e-01 1.257e-01 2.742 0.00839 **
## scale(bias_drone) 3.026e-01 1.257e-01 2.406 0.01977 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9134 on 51 degrees of freedom
## Multiple R-squared: 0.1971, Adjusted R-squared: 0.1656
## F-statistic: 6.261 on 2 and 51 DF, p-value: 0.003704
##
## zero_order partial part
## agreeableness 0.33 0.36 0.34
## bias_drone 0.28 0.32 0.30
##
## Call:
## omcdiag(x = vars %>% select(n), y = vars %>% select(dv))
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.9959 0
## Farrar Chi-Square: 0.2100 0
## Red Indicator: 0.0638 0
## Sum of Lambda Inverse: 2.0082 0
## Theil's Method: -0.1890 0
## Condition Number: 12.8855 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = vars %>% select(n), y = vars %>% select(dv))
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein IND1 IND2
## agreeableness 1.0041 0.9959 0.2124 Inf 0.998 0.9888 0 0.0192 1
## bias_drone 1.0041 0.9959 0.2124 Inf 0.998 0.9888 0 0.0192 1
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## * all coefficients have significant t-ratios
##
## R-square of y on all x: 0.1971
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## [1] "DV = helpful_exchange"
##
## Call:
## lm(formula = fm, data = var)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7791 -0.5876 0.0831 0.5370 1.7204
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.027e-16 1.221e-01 0.000 1.00000
## scale(agreeableness) 3.777e-01 1.342e-01 2.814 0.00697 **
## scale(aus_born_co_driver) 1.334e-01 1.477e-01 0.903 0.37074
## scale(eng_fl_co_driver) 9.313e-02 1.505e-01 0.619 0.53878
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8972 on 50 degrees of freedom
## Multiple R-squared: 0.2406, Adjusted R-squared: 0.195
## F-statistic: 5.279 on 3 and 50 DF, p-value: 0.003056
##
## zero_order partial part
## agreeableness 0.45 0.37 0.35
## aus_born_co_driver 0.30 0.13 0.11
## eng_fl_co_driver 0.30 0.09 0.08
##
## Call:
## omcdiag(x = vars %>% select(n), y = vars %>% select(dv))
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.6020 0
## Farrar Chi-Square: 25.9666 1
## Red Indicator: 0.4179 0
## Sum of Lambda Inverse: 4.1129 0
## Theil's Method: 0.3086 0
## Condition Number: 17.7600 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = vars %>% select(n), y = vars %>% select(dv))
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein IND1
## agreeableness 1.1857 0.8434 4.7358 9.6573 0.9184 1.4793 0 0.0331
## aus_born_co_driver 1.4364 0.6962 11.1277 22.6918 0.8344 1.7921 1 0.0273
## eng_fl_co_driver 1.4908 0.6708 12.5161 25.5230 0.8190 1.8600 1 0.0263
## IND2
## agreeableness 0.5950
## aus_born_co_driver 1.1542
## eng_fl_co_driver 1.2508
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## aus_born_co_driver , eng_fl_co_driver , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.2406
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## [1] "DV = terrible_codriver"
##
## Call:
## lm(formula = fm, data = var)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4445 -0.4996 -0.0833 0.3611 2.7814
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06152 0.11005 -0.559 0.5788
## scale(bias_drone) 0.25612 0.11237 2.279 0.0271 *
## scale(eng_fl_co_driver) -0.10096 0.11857 -0.851 0.3987
## scale(incongruent_time_drone) -0.16625 0.11456 -1.451 0.1532
## scale(intellect_drone) 0.18504 0.11547 1.603 0.1156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8007 on 48 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.2261, Adjusted R-squared: 0.1617
## F-statistic: 3.507 on 4 and 48 DF, p-value: 0.01367
##
## zero_order partial part
## bias_drone 0.32 0.31 0.29
## eng_fl_co_driver -0.24 -0.12 -0.11
## incongruent_time_drone -0.28 -0.21 -0.18
## intellect_drone 0.24 0.23 0.20
##
## Call:
## omcdiag(x = vars %>% select(n), y = vars %>% select(dv))
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.8676 0
## Farrar Chi-Square: 7.0747 0
## Red Indicator: 0.1562 0
## Sum of Lambda Inverse: 4.2872 0
## Theil's Method: -0.4124 0
## Condition Number: 32.7085 1
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = vars %>% select(n), y = vars %>% select(dv))
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein IND1
## bias_drone 1.0433 0.9585 0.7069 1.0820 0.9790 1.1483 0 0.0587
## eng_fl_co_driver 1.1102 0.9008 1.7995 2.7543 0.9491 1.2219 0 0.0551
## incongruent_time_drone 1.0644 0.9395 1.0519 1.6100 0.9693 1.1715 0 0.0575
## intellect_drone 1.0693 0.9352 1.1324 1.7333 0.9670 1.1770 0 0.0573
## IND2
## bias_drone 0.6237
## eng_fl_co_driver 1.4920
## incongruent_time_drone 0.9096
## intellect_drone 0.9748
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## eng_fl_co_driver , incongruent_time_drone , intellect_drone , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.2261
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
|
|
|
## [1] "DV = collisions_overall"
##
## Call:
## lm(formula = fm, data = var)
##
## Residuals:
## Min 1Q Median 3Q Max
## -137.26 -49.20 -13.43 33.23 292.60
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 165.46 12.36 13.384 <2e-16 ***
## scale(incongruent_errors) 40.91 12.59 3.248 0.0021 **
## scale(inconsistent_codriver) 13.29 13.40 0.992 0.3261
## scale(prop_female) 32.52 12.65 2.570 0.0133 *
## scale(terrible_codriver) 27.36 13.31 2.056 0.0451 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 90.85 on 49 degrees of freedom
## Multiple R-squared: 0.3515, Adjusted R-squared: 0.2985
## F-statistic: 6.639 on 4 and 49 DF, p-value: 0.000237
##
## zero_order partial part
## incongruent_errors 0.37 0.42 0.37
## inconsistent_codriver 0.28 0.14 0.11
## prop_female 0.32 0.34 0.30
## terrible_codriver 0.32 0.28 0.24
##
## Call:
## omcdiag(x = vars %>% select(n), y = vars %>% select(dv))
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.8516 0
## Farrar Chi-Square: 8.1628 0
## Red Indicator: 0.1618 0
## Sum of Lambda Inverse: 4.3369 0
## Theil's Method: -0.7556 0
## Condition Number: 4.9025 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = vars %>% select(n), y = vars %>% select(dv))
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein IND1
## incongruent_errors 1.0185 0.9818 0.3085 0.4720 0.9909 1.1464 0 0.0589
## inconsistent_codriver 1.1533 0.8671 2.5545 3.9084 0.9312 1.2981 0 0.0520
## prop_female 1.0281 0.9726 0.4688 0.7173 0.9862 1.1572 0 0.0584
## terrible_codriver 1.1370 0.8795 2.2830 3.4931 0.9378 1.2798 0 0.0528
## IND2
## incongruent_errors 0.2432
## inconsistent_codriver 1.7785
## prop_female 0.3661
## terrible_codriver 1.6122
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## inconsistent_codriver , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.3515
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## [[1]]
##
## Call:
## lm(formula = fm, data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -142.87 -46.20 -14.95 31.50 290.29
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 165.054 12.583 13.118 < 2e-16 ***
## inconsistent_codriver 12.771 13.681 0.933 0.35524
## prop_female 32.188 12.840 2.507 0.01562 *
## incongruent_errors 40.731 12.734 3.199 0.00245 **
## terrible_codriver 27.239 13.444 2.026 0.04832 *
## inconsistent_codriver:prop_female 3.322 12.887 0.258 0.79765
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 91.73 on 48 degrees of freedom
## Multiple R-squared: 0.3524, Adjusted R-squared: 0.2849
## F-statistic: 5.224 on 5 and 48 DF, p-value: 0.0006588
##
##
## [[2]]
##
## Call:
## lm(formula = fm, data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -135.24 -55.10 -12.84 34.41 291.74
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 166.559 12.594 13.225 < 2e-16 ***
## terrible_codriver 29.824 14.069 2.120 0.03922 *
## prop_female 32.376 12.743 2.541 0.01435 *
## incongruent_errors 41.449 12.717 3.259 0.00206 **
## inconsistent_codriver 13.386 13.495 0.992 0.32621
## terrible_codriver:prop_female -9.455 16.497 -0.573 0.56922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 91.48 on 48 degrees of freedom
## Multiple R-squared: 0.3559, Adjusted R-squared: 0.2888
## F-statistic: 5.304 on 5 and 48 DF, p-value: 0.0005859
##
##
## [[3]]
## NULL
## [1] "DV = speed_overall"
##
## Call:
## lm(formula = fm, data = var)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0103 -0.6989 0.0295 0.7637 3.0783
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.3420 0.1657 50.336 < 2e-16 ***
## scale(agreeableness) -0.1427 0.1948 -0.733 0.46739
## scale(helpful_exchange) -0.4041 0.1989 -2.032 0.04785 *
## scale(inconsistent_codriver) -0.1542 0.1808 -0.853 0.39815
## scale(neuroticism_drone) 0.1797 0.1776 1.012 0.31694
## scale(prop_female) -0.5493 0.1735 -3.165 0.00272 **
## scale(switch_time_drone) 0.3961 0.1734 2.284 0.02693 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.218 on 47 degrees of freedom
## Multiple R-squared: 0.4417, Adjusted R-squared: 0.3704
## F-statistic: 6.198 on 6 and 47 DF, p-value: 7.598e-05
##
## zero_order partial part
## agreeableness -0.33 -0.11 -0.08
## helpful_exchange -0.40 -0.28 -0.22
## inconsistent_codriver -0.27 -0.12 -0.09
## neuroticism_drone 0.27 0.15 0.11
## prop_female -0.47 -0.42 -0.34
## switch_time_drone 0.31 0.32 0.25
##
## Call:
## omcdiag(x = vars %>% select(n), y = vars %>% select(dv))
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.5590 0
## Farrar Chi-Square: 29.1814 1
## Red Indicator: 0.2071 0
## Sum of Lambda Inverse: 7.2164 0
## Theil's Method: -1.2559 0
## Condition Number: 23.7627 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = vars %>% select(n), y = vars %>% select(dv))
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein IND1
## agreeableness 1.3560 0.7374 3.4179 4.3614 0.8587 2.7864 0 0.0768
## helpful_exchange 1.4135 0.7074 3.9699 5.0658 0.8411 2.9045 0 0.0737
## inconsistent_codriver 1.1683 0.8560 1.6154 2.0613 0.9252 2.4006 0 0.0892
## neuroticism_drone 1.1277 0.8868 1.2257 1.5641 0.9417 2.3171 0 0.0924
## prop_female 1.0762 0.9292 0.7312 0.9330 0.9640 2.2113 0 0.0968
## switch_time_drone 1.0747 0.9305 0.7171 0.9151 0.9646 2.2083 0 0.0969
## IND2
## agreeableness 1.6536
## helpful_exchange 1.8426
## inconsistent_codriver 0.9072
## neuroticism_drone 0.7131
## prop_female 0.4458
## switch_time_drone 0.4378
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## agreeableness , inconsistent_codriver , neuroticism_drone , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.4417
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================
## [[1]]
##
## Call:
## lm(formula = fm, data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.99365 -0.67991 0.03463 0.76709 3.07569
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.33963 0.16891 49.374 < 2e-16 ***
## inconsistent_codriver -0.15697 0.18448 -0.851 0.39925
## prop_female -0.55078 0.17592 -3.131 0.00303 **
## agreeableness -0.14332 0.19695 -0.728 0.47050
## helpful_exchange -0.40457 0.20106 -2.012 0.05007 .
## neuroticism_drone 0.18333 0.18253 1.004 0.32046
## switch_time_drone 0.39728 0.17559 2.263 0.02843 *
## inconsistent_codriver:prop_female 0.01954 0.17689 0.110 0.91251
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.231 on 46 degrees of freedom
## Multiple R-squared: 0.4419, Adjusted R-squared: 0.3569
## F-statistic: 5.203 on 7 and 46 DF, p-value: 0.0002031
##
##
## [[2]]
## NULL
##
## [[3]]
##
## Call:
## lm(formula = fm, data = var_std)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0108 -0.6706 0.0411 0.7627 3.1151
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.33688 0.16993 49.061 < 2e-16 ***
## helpful_exchange -0.40841 0.20241 -2.018 0.04947 *
## prop_female -0.55348 0.17693 -3.128 0.00305 **
## agreeableness -0.14074 0.19715 -0.714 0.47893
## inconsistent_codriver -0.15784 0.18385 -0.859 0.39505
## neuroticism_drone 0.17975 0.17950 1.001 0.32188
## switch_time_drone 0.39984 0.17648 2.266 0.02822 *
## helpful_exchange:prop_female 0.03525 0.19732 0.179 0.85899
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.231 on 46 degrees of freedom
## Multiple R-squared: 0.4421, Adjusted R-squared: 0.3572
## F-statistic: 5.208 on 7 and 46 DF, p-value: 0.0002014
## [1] "DV = distance_overall"
##
## Call:
## lm(formula = fm, data = var)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2490.5 -793.7 -221.9 547.3 3811.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12642.0 185.8 68.031 < 2e-16 ***
## scale(incongruent_errors) 386.7 203.6 1.899 0.06347 .
## scale(inconsistent_codriver) 179.3 200.7 0.893 0.37600
## scale(switch_cost) -415.6 203.3 -2.044 0.04630 *
## scale(terrible_codriver) 581.7 199.7 2.913 0.00538 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1366 on 49 degrees of freedom
## Multiple R-squared: 0.3389, Adjusted R-squared: 0.285
## F-statistic: 6.281 on 4 and 49 DF, p-value: 0.000367
##
## zero_order partial part
## incongruent_errors 0.34 0.26 0.22
## inconsistent_codriver 0.27 0.13 0.10
## switch_cost -0.34 -0.28 -0.24
## terrible_codriver 0.38 0.38 0.34
##
## Call:
## omcdiag(x = vars %>% select(n), y = vars %>% select(dv))
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.7455 0
## Farrar Chi-Square: 14.9315 1
## Red Indicator: 0.2142 0
## Sum of Lambda Inverse: 4.6313 0
## Theil's Method: -0.4725 0
## Condition Number: 3.5825 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
##
## Call:
## imcdiag(x = vars %>% select(n), y = vars %>% select(dv))
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein IND1
## incongruent_errors 1.1787 0.8484 2.9776 4.5557 0.9211 1.4361 0 0.0509
## inconsistent_codriver 1.1447 0.8736 2.4110 3.6888 0.9347 1.3947 0 0.0524
## switch_cost 1.1746 0.8514 2.9094 4.4513 0.9227 1.4312 0 0.0511
## terrible_codriver 1.1334 0.8823 2.2240 3.4027 0.9393 1.3811 0 0.0529
## IND2
## incongruent_errors 1.1139
## inconsistent_codriver 0.9287
## switch_cost 1.0922
## terrible_codriver 0.8652
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## incongruent_errors , inconsistent_codriver , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.3389
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================